library(tidyverse) # for graphing and data cleaning
library(googlesheets4) # for reading googlesheet data
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(geofacet) # for special faceting with US map layout
gs4_deauth() # To not have to authorize each time you knit.
theme_set(theme_minimal()) # My favorite ggplot() theme :)
#Lisa's garden data
garden_harvest <- read_sheet("https://docs.google.com/spreadsheets/d/1DekSazCzKqPS2jnGhKue7tLxRU3GVL1oxi-4bEM5IWw/edit?usp=sharing") %>%
mutate(date = ymd(date))
# Seeds/plants (and other garden supply) costs
supply_costs <- read_sheet("https://docs.google.com/spreadsheets/d/1dPVHwZgR9BxpigbHLnA0U99TtVHHQtUzNB9UR0wvb7o/edit?usp=sharing",
col_types = "ccccnn")
# Planting dates and locations
plant_date_loc <- read_sheet("https://docs.google.com/spreadsheets/d/11YH0NtXQTncQbUse5wOsTtLSKAiNogjUA21jnX5Pnl4/edit?usp=sharing",
col_types = "cccnDlc")%>%
mutate(date = ymd(date))
# Tidy Tuesday data
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week. Display the results so that the vegetables are rows but the days of the week are columns.garden_harvest %>%
mutate(day_of_week = wday(date, label = TRUE)) %>%
group_by(day_of_week, vegetable) %>%
summarise(tot_harvest_lbs = sum(weight)*0.00220462) %>%
pivot_wider(id_cols= vegetable,
names_from = day_of_week,
values_from = tot_harvest_lbs)
garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot variable from the plant_date_loc table. This will not turn out perfectly. What is the problem? How might you fix it?garden_harvest %>%
group_by(variety, vegetable) %>%
summarise(tot_harvest_lbs = sum(weight)*0.00220462) %>%
left_join(plant_date_loc,
by = c("vegetable", "variety"))
The total harvest was not kept track of by plot for garden harvest. Therefore, when the two data sets are merged, the total harvest for each plot is just assumed to be the same for each variety and vegetable which it is not. This can be fixed by adding a plot column to garden harvest data set to keep track of tot harvest weight from each plot.
garden_harvest and supply_cost datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.You first would join the garden_harvest data set with the supply_cost datasets
garden_harvest %>%
filter(vegetable == "tomatoes") %>%
mutate(variety_2= fct_reorder(variety, date, min)) %>%
group_by(variety_2) %>%
summarise(total_harvest_lbs = sum(weight)*0.00220462) %>%
ggplot(aes(y = variety_2,
x = total_harvest_lbs)) +
geom_col()
garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().garden_harvest %>%
mutate(var_lower = str_to_lower(variety),
var_length = str_length(var_lower)) %>%
arrange(vegetable, var_length) %>%
distinct(vegetable, variety, .keep_all = TRUE)
garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().garden_harvest %>%
mutate(has_er_ar = str_detect(variety, "er|ar")) %>%
filter(has_er_ar == TRUE) %>%
distinct(vegetable, variety)
In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.
{300px}
{300px}
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.
It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:
sdate. Use geom_density().Trips %>%
ggplot(aes(x = sdate)) +
geom_density(fill = "lightgreen",
color = "lightgreen") +
labs(x = "Date",
y = "") +
theme_minimal()
This distribution is right skewed with a peaks around the beginning of October and the end of October. It seems like from oct-nov is when people used the bikes the most.
mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.Trips %>%
mutate(time_of_day = hour(sdate)+minute(sdate)/60) %>%
ggplot(aes(x = time_of_day)) +
geom_density(fill = "green",
color = "green") +
labs(x = "Time Of Day",
y = "")
The bikes are used and rented the most in the morning and towards the end of the day (rush hours).
Trips %>%
mutate(day_of_week = wday(sdate, label = TRUE)) %>%
ggplot(aes(y = day_of_week)) +
geom_bar(fill = "lightgreen",
color = "lightgreen") +
labs(x = "Trips",
y = "")
People use the bikes a lot throughout the week and less so on the weekend (sat and sun).
Trips %>%
mutate(time_of_day = hour(sdate)+minute(sdate)/60,
day_of_week = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time_of_day)) +
geom_density(fill = "green",
color = "green") +
facet_wrap(vars(day_of_week)) +
labs(x = "Trips",
y = "")
Yes, the weekdays look pretty much the same as do the weekend days. This makes sense because of rush hours.
The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises. Repeat the graphic from Exercise @ref(exr:exr-temp) (d) with the following changes:
fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.Trips %>%
mutate(time_of_day = hour(sdate)+minute(sdate)/60,
day_of_week = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time_of_day)) +
geom_density(aes(fill = client),
color = NA,
alpha = .5) +
facet_wrap(vars(day_of_week)) +
labs(x = "Time Of Day",
y = "")
Registered bikers use the bikes to get to work whereas casual bikers use them more leisurely.
position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?Trips %>%
mutate(time_of_day = hour(sdate)+minute(sdate)/60,
day_of_week = wday(sdate, label = TRUE)) %>%
ggplot(aes(x = time_of_day)) +
geom_density(aes(fill = client),
color = NA,
alpha = .5,
position = position_stack()) +
facet_wrap(vars(day_of_week)) +
labs(x = "Time Of Day",
y = "")
The difference between 11 and 12 is that the casual and registered bikers are stacked.
weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.Trips %>%
mutate(time_of_day = hour(sdate)+minute(sdate)/60,
day_of_week = wday(sdate, label = TRUE),
wknd = ifelse(day_of_week %in% c("Sat", "Sun"), "Weekend", "Weekday"))%>%
ggplot(aes(x = time_of_day)) +
geom_density(aes(fill = client),
color = NA,
alpha = .5,
position = position_stack()) +
facet_wrap(vars(wknd)) +
labs(x = "Time of Day",
y = "")
Showing the number of causal and registered clients that use the bikes by splitting into 2 different graphs, one for weekday and one for weekend.
client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?Trips %>%
mutate(time_of_day = hour(sdate)+minute(sdate)/60,
day_of_week = wday(sdate, label = TRUE),
wknd = ifelse(day_of_week %in% c("Sat", "Sun"), "Weekend", "Weekday"))%>%
ggplot(aes(x = time_of_day)) +
geom_density(aes(fill = wknd),
color = NA,
alpha = .5,
position = position_stack()) +
facet_wrap(vars(client)) +
labs(x = "Time of Day",
y = "")
This is the same as above but instead of splitting into weekend and weekday, it is split into casual and registered. The colors indicate the weekend or weekday.
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!Trips %>%
left_join(Stations,
by = c("sstation" = "name")) %>%
group_by(sstation) %>%
summarize(num_dep = n(), lat, long) %>%
ggplot(aes(y = lat, x = long,
color = num_dep)) +
geom_point()
Trips %>%
inner_join(Stations,
by = c("sstation" = "name")) %>%
select(sstation, lat, long, client) %>%
group_by(sstation) %>%
summarize(prop_cas = mean(client == "Casual"), lat, long) %>%
ggplot(aes(y = lat, x = long,
color = prop_cas)) +
geom_point()
I see that where the proportion of casual clients are is condenced in the middle of the stations groupings.
as_date(sdate) converts sdate from date-time format to date format.top_trip_dates <- Trips %>%
mutate(date = as_date(sdate)) %>%
count(sstation, date) %>%
arrange(desc(n)) %>%
rename(new_n = n) %>%
slice_max(n = 10, order_by = new_n)
Trips %>%
mutate(date = as_date(sdate)) %>%
inner_join(top_trip_dates,
by = c("sstation", "date"))
Trips %>%
mutate(date = as_date(sdate)) %>%
inner_join(top_trip_dates,
by = c("sstation", "date")) %>%
mutate(day_of_week = wday(date, label = TRUE)) %>%
group_by(day_of_week, client) %>%
summarize(count = n()) %>%
group_by(client) %>%
mutate(total = sum(count), prop = count / total) %>%
pivot_wider(id_cols = day_of_week,
names_from = client,
values_from = prop)
DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.
#I need to talk to you about this in office hours, still not sure how to get the git to work
This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.
facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.## I have reached my max capacity for coding by this problem, I am sorry Prof. Lendway! I will attempt future challenge problems.
DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?